I was at Tufts University and I happened to get connected with Thomas Trikalinos and Joseph Lau, and Chris Schmid. And at the time I was working on machine learning and natural language processing stuff. And it occurred to all of us jointly, along with my advisor, Carla Brodley, that perhaps we could expedite some of the processes that were being done manually in evidence synthesis using machine learning and natural image processing. So ever since then I've been working on that. The aspect that's received the most attention is probably on the screening side. So this is where you're identifying the literature, the evidence to be included in the systematic review that you're performing. And there's been a lot of work that's looked at using automation techniques, and in particular, automatic classification techniques to expedite that process. And I think those technologies are actually pretty well established. And I think the general consensus is that you can reduce the labor by about half without sacrificing sensitivity or recall to the relevant evidence, which is really important. So these tools, we've developed some of them and other people as well, including for example James Thomas and his group. Our stuff you can find at Abstrackr, so it's free to use and it's open source. But again, that's just one one instance, and I think generally these kinds of technologies have been helpful. The other bit that's kind of more emerging, I think, are methods for automating the actual extraction of structured information from publications describing the results of trials and other findings. And this I think is still in the research phases, but it's certainly something that I'm working on and a bunch of other people are as well. But from my own plug I would say we have this thing called RobotReviewer that we've been working on that does a lot of this and tries to automate things like risk bias appraisal. Julian Higgins has also done similar things with one of his students. So I think there's a lot of interest in this space generally. So it'll be exciting to see where it all goes.